diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md index 91e50ccfecec4..0890cc31dd98b 100644 --- a/docs/mllib-guide.md +++ b/docs/mllib-guide.md @@ -34,6 +34,7 @@ We list major functionality from both below, with links to detailed guides. * [correlations](mllib-statistics.html#correlations) * [stratified sampling](mllib-statistics.html#stratified-sampling) * [hypothesis testing](mllib-statistics.html#hypothesis-testing) + * [streaming significance testing](mllib-statistics.html#streaming-significance-testing) * [random data generation](mllib-statistics.html#random-data-generation) * [Classification and regression](mllib-classification-regression.html) * [linear models (SVMs, logistic regression, linear regression)](mllib-linear-methods.html) diff --git a/docs/mllib-statistics.md b/docs/mllib-statistics.md index ade5b0768aefe..de209f68e19ca 100644 --- a/docs/mllib-statistics.md +++ b/docs/mllib-statistics.md @@ -521,6 +521,31 @@ print(testResult) # summary of the test including the p-value, test statistic, +### Streaming Significance Testing +MLlib provides online implementations of some tests to support use cases +like A/B testing. These tests may be performed on a Spark Streaming +`DStream[(Boolean,Double)]` where the first element of each tuple +indicates control group (`false`) or treatment group (`true`) and the +second element is the value of an observation. + +Streaming significance testing supports the following parameters: + +* `peacePeriod` - The number of initial data points from the stream to +ignore, used to mitigate novelty effects. +* `windowSize` - The number of past batches to perform hypothesis +testing over. Setting to `0` will perform cumulative processing using +all prior batches. + + +